Citation: Saha, D.K.; Hoque, M.E.;
Badihi, H. Development of Intelligent
Fault Diagnosis Technique of Rotary
Machine Element Bearing: A
Machine Learning Approach. Sensors
2022, 22, 1073. https://doi.org/
10.3390/s22031073
Academic Editor: Steven Chatterton
Received: 26 December 2021
Accepted: 27 January 2022
Published: 29 January 2022
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Article
Development of Intelligent Fault Diagnosis Technique of
Rotary Machine Element Bearing: A Machine
Learning Approach
Dip Kumar Saha
1
, Md. Emdadul Hoque
2
and Hamed Badihi
3,
*
1
Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology,
Rajshahi 6204, Bangladesh; dip07me@gmail.com
2
Department of Mechanical Engineering, Rajshahi University of Engineering & Technology,
Rajshahi 6204, Bangladesh; mehoque@me.ruet.ac.bd
3
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics (NUAA),
Nanjing 211106, China
* Correspondence: hamed.badihi@nuaa.edu.cn
Abstract:
The bearing is an essential component of a rotating machine. Sudden failure of the bearing
may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault
diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing.
An experimental setup was designed and developed to generate faulty data in various conditions,
such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The
time waveform of raw vibration data generated from the system was transformed into a frequency
spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect
the defective bearing. Another significant contribution of this paper is the application of a machine
learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as
the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and
optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the
model performance. The classification accuracy obtained using SVM with a traditional grid search
cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based
SVM was found to be 93.9%. The developed model was also compared with other traditional ML
techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis
(LDA). In every case, the proposed model outperformed the existing algorithms.
Keywords:
support vector machine (SVM); particle swarm optimization (PSO); fault diagnosis;
ball bearing; machine learning (ML)
1. Introduction
Machines are the heart of any industrial unit or manufacturing plant. Numerous types
of machinery are available in the industry. The profit of any production plant is highly
dependent on the available runtime of machines. A reduction in downtime is essential to
increase the company’s profit margin because maintenance costs carry about 15–20% of
total production costs [
1
]. However, it is a reality that almost 30% of maintenance costs
are simply wasted due to improper maintenance strategy adoption and failure to perform
maintenance at appropriate times. The sudden collapse of machine components may lead
to substantial production losses. Proper condition monitoring of these components is
essential to ensure the uninterrupted operation of industries. Condition monitoring deals
with both present and past aspects of the machines. Various forms of information such as
vibration, noise, temperature, current drawn by the motor, and lubricating oil conditions
are obtained from the machines during this process. Obviously, this information can play a
significant role in developing a suitable maintenance strategy.
Sensors 2022, 22, 1073. https://doi.org/10.3390/s22031073 https://www.mdpi.com/journal/sensors
